相关论文: INDCOR White Paper 5: Addressing Societal Issues i…
The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized checklists to document datasets. This paper expands that field…
Modern cyber security operations collect an enormous amount of logging and alerting data. While analysts have the ability to query and compute simple statistics and plots from their data, current analytical tools are too simple to admit…
Very little has been explored about the narrative as a process when constructing entertainment for interactive media. Simultaneously, the interest in narrative vehicles increases while certain occupations, seeing the narrative as a…
Climate change is one of the most pressing challenges of the 21st century, sparking widespread discourse across social media platforms. Activists, policymakers, and researchers seek to understand public sentiment and narratives while access…
Data storytelling workflows ask learners to integrate analytical, design, and narrative skills, but instructors rarely have the capacity to provide detailed feedback at each step. Computational and AI-assisted storytelling offers…
The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a…
Graph Neural Networks (GNNs) excel in graph-based learning tasks, but their complex, non-linear operations often render them as opaque "black boxes". This opacity hinders user trust, complicates debugging, bias detection, and adoption in…
In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more…
Narratives play a vital role in shaping public perceptions and policy on emerging technologies like quantum technology (QT). However, little is known about the construction and variation of QT narratives across societal domains. This study…
We present "Narrative Weaver", a novel framework that addresses a fundamental challenge in generative AI: achieving multi-modal controllable, long-range, and consistent visual content generation. While existing models excel at generating…
Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications. However, applying explainability and human-in-the-loop methods requires technical proficiency. Despite existing…
Digital health has strong potential for promoting physical activity (PA), yet interventions often fail to sustain engagement among culturally and linguistically diverse (CALD) women. Prior reviews focus on short-term efficacy or…
As AI systems become prevalent in high stakes domains such as surveillance and healthcare, researchers now examine how to design and implement them in a safe manner. However, the potential harms caused by systems to stakeholders in complex…
Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload. The critical challenge is the lack of widely recognized…
A vast area of research in historical science concerns the documentation and study of artefacts and related evidence. Current practice mostly uses spreadsheets or simple relational databases to organise the information as rows with multiple…
The urgency of climate change is now recognized globally. As humanity confronts the critical need to mitigate climate change and foster sustainability, data visualization emerges as a powerful tool with a unique capacity to communicate…
For AI technology to fulfill its full promises, we must have effective means to ensure Responsible AI behavior and curtail potential irresponsible use, e.g., in areas of privacy protection, human autonomy, robustness, and prevention of…
Deep learning model design, development, and debugging is a process driven by best practices, guidelines, trial-and-error, and the personal experiences of model developers. At multiple stages of this process, performance and internal model…
The complexity and diversity of today's media landscape provides many challenges for researchers studying news producers. These producers use many different strategies to get their message believed by readers through the writing styles they…
Based upon a study of how to capture data from Internet of Things (IoT) devices, this paper explores the challenges for data centric design ethnography. Often purchased to perform specific tasks, IoT devices exist in a complex ecosystem.…